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Record W1904935213 · doi:10.5195/ijms.2013.12

Developing a Protocol for Medical Student-Organized Community-Based Hypertension Screening Programs

2013· article· en· W1904935213 on OpenAlexaff
Vishal Varshney, Tyrone G. Harrison, Michal Szymczakowski, Matthew Grossi, Charlotte Jones

Bibliographic record

VenueInternational Journal of Medical Students · 2013
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersCenter of Mathematical Sciences and Applications, Harvard University
KeywordsBlood pressureProtocol (science)MedicineContext (archaeology)Medical physicsComputer scienceInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background: Hypertension screening programs have been effective in raising awareness and identifying people who are otherwise unfamiliar with their disease. We aimed to develop a resource-minimal, evidence-based protocol for a novel medical student-organized hypertension-screening program capable of community implementation. Methods: Eighty-one medical students had their blood pressure measured once using an automated machine and once using the manual auscultatory method. Bland-Altman plots compared agreement between measurement techniques. Results: No significant difference between manual and automated techniques was noted in the measurement of systolic blood pressure, but was noted for diastolic blood pressure. Conclusions: In the context of a community-based screening program, automated and manual measurements may be used interchangeably to obtain an accurate measure of systolic blood pressure. A medical student-organized community-screening program is an effective way to screen large numbers of people in a short amount of time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.118
GPT teacher head0.445
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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